User-Guided Path Planning for Redundant Manipulators in Highly Constrained Work Environments

Abstract

We present a bi-directional tree-search framework for point-to-point path planning for manipulators. By design, it integrates human assistance seamlessly. Our framework consists of six modules: tree selection, focus selection, node selection, target selection, extend selection and connection type selection. Each module consists of a set of interchangeable strategies. By exploiting interaction among these strategies and selecting appropriate strategies based on the contextual cues from the search state, our method computes high quality solutions in a variety of complex scenarios with a low failure rate. We compare our approach with popular methods in a set of very hard scenarios. Without human assistance, our approach reduces the failure rate drastically. With human assistance, our approach has a zero failure rate as well as high solution quality.